Model based diagnosis of nonlinear process systems
A method has been proposed for the analysis of model-based diagnosis
algorithms of nonlinear process systems. Phyisical models have been
used for the description of process dynamics and semi-empirical models
have been used for the description of the fault phenomena. It has
been shown that the performance of the fault detection and isolation
algorithms is improving with the increasing level of detail of the
process models [1]. It has been shown that safe simultaneous fault
detection and isolation is possible using the grey- or white-box models
of the faults together with the process model [2,3].

Analysis of nonlinear process systems
It has been shown that a wide class of fed-batch bioreactors is not
controllable in the nonlinear sense when the manipulable input is
the most frequently used inlet feed flow rate [4]. It has been computed
using nonlinear coordinates transformations that the zero dynamics
of isotherm continuous bioreactors is asymptotically stable in the
case of several different reaction kinetics if the controlled output
is the substrate concentration [5]. Using the analysis results, globally
stabilizing nonlinear controllers have been designed for different
kinds of bioreactors.

Hamiltonian description of process systems
It has been shown that a wide class of process systems can be represented
in the Hamiltonian canonical form which is well-known from classical
mechanics [6]. The conditions of the dissipative- Hamiltonian description
of dynamical systems in quasi-polynomial form with a quadratic Hamiltonian
function have been given. It has also been shown that the global stability
of quasi-polynomial systems with the well-known entropy-like Lyapunov
function is equivalent to the existence of this dissipative-Hamiltonian
description [7].